Estimate energy demand (Gross Energy) - Tier 2

Calculate gross energy (GE) intake per IPCC 2019 Tier 2 equations (Vol 4, Ch 10). Estimates net energy components for maintenance, activity, lactation, work, pregnancy, growth, and wool, then derives total gross energy using the REM/REG ratio approach from IPCC Eq 10.16.

All coefficients come from internal package data.

Default weight gain and pregnancy fraction follow the cohort's life stage: mature cohorts ("Adult Female", "Adult Male", "Adult", "Sows", "Boars", "Layers") gain no weight, and only mature-female cohorts take the species pregnancy fraction (IPCC 2019 Vol 4 Ch 10, pp. 10.18 and 10.28). Beef breeding cows ("Adult Female" of non-dairy cattle) take the "Pregnant, %" of the IPCC 2019 Table 10A.2 "Mature Females" row for their IPCC region, resolved from iso3 or area_code. A cohort of NA or "All" is a whole-herd row and keeps both species defaults. Any other cohort label aborts. Values the caller supplies are kept.

Usage

estimate_energy_demand(
  data,
  method = "ipcc2019",
  lactation_method = c("milk_composition", "ipcc2019")
)

Arguments

data

A dataframe with columns species, cohort, heads, and optionally iso3. Optional production columns: weight, milk_yield_kg_day, fat_percent, weight_gain_kg_day, work_hours_day, work_coef, cfi, pregnant_fraction, temperature_c, diet_quality, grazing_distance_km, system. work_coef overrides the joined IPCC work coefficient (cw, 0 by default for every species) for rows that need draught/work energy (IPCC Eq 10.11) without changing the global default. cfi overrides the joined maintenance coefficient (cfi_mj_day_kg075, IPCC Eq 10.3) for rows whose herd-average maintenance requirement is known from a national inventory (e.g. a housed dairy herd calibrated to a Zootecnicas/NIR Cfi), without changing the global default.

method

Method for calculation (default "ipcc2019").

lactation_method

How net energy for lactation (NEl) is derived from milk yield. One of:

• "milk_composition" (default): the NRC (2001) milk-energy equation, NEl = Milk * (0.389 * Fat + 0.229 * Protein + 0.165 * Lactose) (MJ/kg: the published Mcal/kg coefficients 0.0929, 0.0547 and 0.0395 times 4.184), for rows with a positive protein and lactose content. Rows without that composition use the "ipcc2019" equations.

• "ipcc2019": IPCC 2019 Refinement Vol 4 Ch 10. Eq 10.8, NEl = Milk * (1.47 + 0.40 * Fat), for cattle, buffalo and other species; Eq 10.9, NEl = Milk * EVmilk, for sheep and goats with the default EVmilk of 4.6 MJ/kg for sheep (7% fat; AFRC 1993, 1995) and 3 MJ/kg for goats (3.8% fat; AFRC 1998). The defaults ignore fat_percent.

The equation used for each row is recorded in method_lactation ("nrc2001_milk_composition", "ipcc2019_eq10_8", "ipcc2019_eq10_9_default_ev", or "none" when there is no milk).

Value

Dataframe with added gross_energy (MJ/day), intermediate net energy components, cohort_life_stage ("mature_female", "mature", "growing", or NA for a whole-herd row), and method_energy, method_lactation and method_pregnancy tracking columns. method_pregnancy is "supplied", "species_default", "species_default_whole_herd", "not_mature_female", "ipcc2019_t10a2" (a beef cow at its region's published rate), "ipcc2019_t10a2_neighbour_assumed" (Western Europe, which the table omits, at the 80% North America and Eastern Europe report) or "ipcc2019_t10a2_mean_assumed" (no resolvable region: the mean of the published regions).

Examples

tibble::tibble(
  species = "Dairy Cattle", cohort = "Adult Female",
  heads = 100, weight = 600, diet_quality = "High",
  milk_yield_kg_day = 20
) |>
  estimate_energy_demand() |>
  dplyr::select(species, cohort, heads, ne_maintenance,
    ne_activity, ne_lactation, ne_growth, gross_energy)
#> # A tibble: 1 × 8
#>   species      cohort    heads ne_maintenance ne_activity ne_lactation ne_growth
#>   <chr>        <chr>     <dbl>          <dbl>       <dbl>        <dbl>     <dbl>
#> 1 Dairy Cattle Adult Fe…   100           46.8        7.96         61.8         0
#> # ℹ 1 more variable: gross_energy <dbl>